{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This Jupyter Notebook has been modified from code originally developed by [Noah Schutte](https://scholar.google.com/citations?user=3bvIOVsAAAAJ&hl=en&oi=ao). Thanks for his contributions."
      ],
      "metadata": {
        "id": "bh6lUQ0cQn3y"
      },
      "id": "bh6lUQ0cQn3y"
    },
    {
      "cell_type": "markdown",
      "id": "bf4bcab0-0277-4b68-bfee-4c9f4cf1a9ed",
      "metadata": {
        "id": "bf4bcab0-0277-4b68-bfee-4c9f4cf1a9ed"
      },
      "source": [
        "## Install (Colab Only)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b682466-d579-4b0f-ace7-da87e0eff6a3",
      "metadata": {
        "id": "6b682466-d579-4b0f-ace7-da87e0eff6a3",
        "outputId": "f108502f-602f-41a3-ad31-db046dcad5b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyepo\n",
            "  Downloading pyepo-0.3.9-py3-none-any.whl (44 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.3/44.3 kB\u001b[0m \u001b[31m598.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pyepo) (1.25.2)\n",
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            "Collecting Pyomo>=6.1.2 (from pyepo)\n",
            "  Downloading Pyomo-6.7.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.8 MB)\n",
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            "\u001b[?25hCollecting gurobipy>=9.1.2 (from pyepo)\n",
            "  Downloading gurobipy-11.0.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (13.4 MB)\n",
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            "Collecting ply (from Pyomo>=6.1.2->pyepo)\n",
            "  Downloading ply-3.11-py2.py3-none-any.whl (49 kB)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.1->pyepo) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.13.1->pyepo)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.1->pyepo) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.13.1->pyepo)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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            "\u001b[?25hCollecting ppft>=1.7.6.8 (from pathos->pyepo)\n",
            "  Downloading ppft-1.7.6.8-py3-none-any.whl (56 kB)\n",
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            "\u001b[?25hCollecting dill>=0.3.8 (from pathos->pyepo)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "\u001b[?25hCollecting pox>=0.3.4 (from pathos->pyepo)\n",
            "  Downloading pox-0.3.4-py3-none-any.whl (29 kB)\n",
            "Collecting multiprocess>=0.70.16 (from pathos->pyepo)\n",
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            "\u001b[?25hRequirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->pyepo) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->pyepo) (3.5.0)\n",
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            "Installing collected packages: ply, gurobipy, Pyomo, ppft, pox, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, pathos, nvidia-cusolver-cu12, pyepo\n",
            "Successfully installed Pyomo-6.7.3 dill-0.3.8 gurobipy-11.0.2 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 pathos-0.3.2 ply-3.11 pox-0.3.4 ppft-1.7.6.8 pyepo-0.3.9\n"
          ]
        }
      ],
      "source": [
        "# install\n",
        "!pip install pyepo\n",
        "!pip install gurobipy"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove the problematic hook from Google Colab."
      ],
      "metadata": {
        "id": "6KEird8Y40kT"
      },
      "id": "6KEird8Y40kT"
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.meta_path = [hook for hook in sys.meta_path if not any(keyword in str(hook) for keyword in [\"google.colab\"])]"
      ],
      "metadata": {
        "id": "Nai33FgV40B9"
      },
      "id": "Nai33FgV40B9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "65247802",
      "metadata": {
        "id": "65247802"
      },
      "source": [
        "In this tutorial, we will train and test end-to-end predict-then-optimize for different approaches."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a0834bb",
      "metadata": {
        "id": "6a0834bb"
      },
      "outputs": [],
      "source": [
        "# set random seed\n",
        "import random\n",
        "random.seed(42)\n",
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "import torch\n",
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cb28d718",
      "metadata": {
        "id": "cb28d718"
      },
      "source": [
        "## 1 Problem Example: Shortest Path"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "753fb690",
      "metadata": {
        "id": "753fb690"
      },
      "source": [
        "Consider a 5x5 grid network again. The figure shows that each node has top, bottom, left, and right neighbors. We aim to find the shortest path from left top to right bottom."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b439ecf",
      "metadata": {
        "id": "5b439ecf"
      },
      "source": [
        "<img src=\"https://github.com/khalil-research/PyEPO/blob/main/images/shortestpath.png?raw=1\" width=\"500\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e8ecd83",
      "metadata": {
        "id": "0e8ecd83"
      },
      "source": [
        "The weighted graph includes 25 nodes and 40 edges. The weights of the edges are the costs of the path. We load the synthetic dataset where the costs $\\mathbf{c}$ are unknown and can be predicted from features $\\mathbf{x}$.\n",
        "\n",
        "``pyepo.data.shortestpath.genData`` allows us to generate data with various size, polynomial degree, and noise. We can learn more about the dataset [here](https://github.com/khalil-research/PyEPO/blob/main/notebooks/02%20Optimization%20Dataset.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae4ba391",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:30.969643569Z",
          "start_time": "2023-07-03T16:15:30.903584585Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ae4ba391",
        "outputId": "2fa0b8da-694c-4875-d440-baf102096064"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Auto-Sklearn cannot be imported.\n"
          ]
        }
      ],
      "source": [
        "import pyepo\n",
        "# generate data\n",
        "# EDIT kNN-loss: we change the num_data from 1000 to 100 and deg from 4 to 6 to more clearly show the positive impact of the kNN loss (less training data and more complicated relationship between features and uncertain objective coefficients)\n",
        "grid = (5,5) # grid size\n",
        "num_data = 100 # number of training data\n",
        "num_feat = 5 # size of feature\n",
        "deg = 6 # polynomial degree\n",
        "e = 0.5 # noise width\n",
        "feats, costs = pyepo.data.shortestpath.genData(num_data+1000, num_feat, grid, deg, e, seed=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8210263f",
      "metadata": {
        "id": "8210263f"
      },
      "source": [
        "Thus, we can use ``optGrbModel`` to build a linear model with the Gurobi solver. For more details on ``optModel``, you can read tutorial [01 Optimization Model\n",
        "](https://github.com/khalil-research/PyEPO/blob/main/notebooks/01%20Optimization%20Model.ipynb) and [documentation](https://khalil-research.github.io/PyEPO/build/html/content/examples/model.html)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d71af1c",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:34.212005726Z",
          "start_time": "2023-07-03T16:15:34.202712580Z"
        },
        "id": "8d71af1c"
      },
      "outputs": [],
      "source": [
        "# build optModel\n",
        "from pyepo.model.grb import optGrbModel\n",
        "\n",
        "class shortestPathModel(optGrbModel):\n",
        "\n",
        "    def __init__(self):\n",
        "        self.grid = (5,5)\n",
        "        self.arcs = self._getArcs()\n",
        "        super().__init__()\n",
        "\n",
        "    def _getArcs(self):\n",
        "        \"\"\"\n",
        "        A helper method to get list of arcs for grid network\n",
        "\n",
        "        Returns:\n",
        "            list: arcs\n",
        "        \"\"\"\n",
        "        arcs = []\n",
        "        for i in range(self.grid[0]):\n",
        "            # edges on rows\n",
        "            for j in range(self.grid[1] - 1):\n",
        "                v = i * self.grid[1] + j\n",
        "                arcs.append((v, v + 1))\n",
        "            # edges in columns\n",
        "            if i == self.grid[0] - 1:\n",
        "                continue\n",
        "            for j in range(self.grid[1]):\n",
        "                v = i * self.grid[1] + j\n",
        "                arcs.append((v, v + self.grid[1]))\n",
        "        return arcs\n",
        "\n",
        "    def _getModel(self):\n",
        "        \"\"\"\n",
        "        A method to build Gurobi model\n",
        "\n",
        "        Returns:\n",
        "            tuple: optimization model and variables\n",
        "        \"\"\"\n",
        "        import gurobipy as gp\n",
        "        from gurobipy import GRB\n",
        "        # ceate a model\n",
        "        m = gp.Model(\"shortest path\")\n",
        "        # varibles\n",
        "        x = m.addVars(self.arcs, name=\"x\")\n",
        "        # sense\n",
        "        m.modelSense = GRB.MINIMIZE\n",
        "        # flow conservation constraints\n",
        "        for i in range(self.grid[0]):\n",
        "            for j in range(self.grid[1]):\n",
        "                v = i * self.grid[1] + j\n",
        "                expr = 0\n",
        "                for e in self.arcs:\n",
        "                    # flow in\n",
        "                    if v == e[1]:\n",
        "                        expr += x[e]\n",
        "                    # flow out\n",
        "                    elif v == e[0]:\n",
        "                        expr -= x[e]\n",
        "                # source\n",
        "                if i == 0 and j == 0:\n",
        "                    m.addConstr(expr == -1)\n",
        "                # sink\n",
        "                elif i == self.grid[0] - 1 and j == self.grid[0] - 1:\n",
        "                    m.addConstr(expr == 1)\n",
        "                # transition\n",
        "                else:\n",
        "                    m.addConstr(expr == 0)\n",
        "        return m, x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "29f84eea",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:38.795439387Z",
          "start_time": "2023-07-03T16:15:38.731606990Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29f84eea",
        "outputId": "397a0076-db16-4291-8bb1-5e4dae3e7005"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restricted license - for non-production use only - expires 2025-11-24\n"
          ]
        }
      ],
      "source": [
        "optmodel = shortestPathModel()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56225c58",
      "metadata": {
        "id": "56225c58"
      },
      "source": [
        "## 2 Dataset and Data Loader"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ebed5419",
      "metadata": {
        "id": "ebed5419"
      },
      "source": [
        "PyTorch provides two data primitives: ``Dataset`` and ``DataLoader``, which allow you to use pre-loaded datasets as well as your own data. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset to enable easy access to the samples. The PyEPO module ``optDataset`` stores features $\\mathbf{x}$ and costs $\\mathbf{c}$, then computes the corresponding optimal solutions $\\mathbf{w}^*$ and objective values $\\mathbf{z}^*$.\n",
        "\n",
        "More details on ``optDataset`` is [here](https://github.com/khalil-research/PyEPO/blob/main/notebooks/02%20Optimization%20Dataset.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9988b779",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:42.978633537Z",
          "start_time": "2023-07-03T16:15:42.964467854Z"
        },
        "id": "9988b779"
      },
      "outputs": [],
      "source": [
        "# split train test data\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, c_train, c_test = train_test_split(feats, costs, test_size=1000, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfcd58dd",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:49.609217956Z",
          "start_time": "2023-07-03T16:15:45.044754480Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfcd58dd",
        "outputId": "8a347a17-eeb6-40bb-fae1-fcc221f87940"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizing for optDataset...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:00<00:00, 256.19it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizing for optDataset...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:05<00:00, 168.96it/s]\n"
          ]
        }
      ],
      "source": [
        "# get optDataset\n",
        "dataset_train = pyepo.data.dataset.optDataset(optmodel, x_train, c_train)\n",
        "dataset_test = pyepo.data.dataset.optDataset(optmodel, x_test, c_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4fc4bb17",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:53.145587193Z",
          "start_time": "2023-07-03T16:15:53.135680577Z"
        },
        "id": "4fc4bb17"
      },
      "outputs": [],
      "source": [
        "# set data loader\n",
        "from torch.utils.data import DataLoader\n",
        "batch_size = 32\n",
        "loader_train = DataLoader(dataset_train, batch_size=batch_size, shuffle=True)\n",
        "loader_test = DataLoader(dataset_test, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f8e78c3-0af2-427d-8f6d-7d7c37294ce2",
      "metadata": {
        "id": "1f8e78c3-0af2-427d-8f6d-7d7c37294ce2"
      },
      "source": [
        "### 2.1 Dataset and Data Loader kNN loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83b0aeea-0e38-4d32-bfb3-e7135be61031",
      "metadata": {
        "id": "83b0aeea-0e38-4d32-bfb3-e7135be61031"
      },
      "source": [
        "The kNN loss is constructed by using a different estimator for $\\mathbb{E}_{\\mathbf{c} \\sim \\mathcal{C}_{\\mathbf{x}} }[\\mathbf{c}|\\mathbf{x}]$, where normally $\\mathbf{c}$ is used. Additionally, the solutions that are considered optimal are different. In practice this comes down to adjusting $\\mathbf{c}$ and $\\mathbf{w^*}$, which is done by using the class ``optDatasetKNN``. The adjusted dataset can just be used as is for any method.\n",
        "\n",
        "[Schutte, N., Postek, K., & Yorke-Smith, N.(2023). Robust losses for decision-focused learning. arXiv 2310.04328.](https://arxiv.org/abs/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "430a39b8-4bdf-455b-a5b4-15074bf940fd",
      "metadata": {
        "id": "430a39b8-4bdf-455b-a5b4-15074bf940fd",
        "outputId": "632d1814-68e3-4233-900c-93201288996d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizing for optDataset...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [00:03<00:00, 27.13it/s]\n"
          ]
        }
      ],
      "source": [
        "# get optDatasetKNN and set kNN data loader\n",
        "dataset_train_knn = pyepo.data.dataset.optDatasetKNN(optmodel, x_train, c_train, k=10, weight=0.5)\n",
        "loader_train_knn = DataLoader(dataset_train_knn, batch_size=batch_size, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7cb4edf9",
      "metadata": {
        "id": "7cb4edf9"
      },
      "source": [
        "## 3 Linear Regression on PyTorch"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f17b53ca",
      "metadata": {
        "id": "f17b53ca"
      },
      "source": [
        "PyTorch is an open-source machine learning library primarily used for developing and training deep learning models such as neural networks. It is developed by Facebook's AI Research lab and is based on the Torch library. PyTorch provides a flexible and intuitive interface for creating and training models.\n",
        "\n",
        "In PyTorch, the ``nn.Module`` is a base class for all neural network modules in the library. It provides a convenient way to organize the layers of a model, and to define the forward pass of the model.\n",
        "\n",
        "Here, we build the simplest PyTorch model, linear regression."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f7e847e0",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:15:57.092548254Z",
          "start_time": "2023-07-03T16:15:57.070646496Z"
        },
        "id": "f7e847e0"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "\n",
        "# build linear model\n",
        "class LinearRegression(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(LinearRegression, self).__init__()\n",
        "        self.linear = nn.Linear(num_feat, (grid[0]-1)*grid[1]+(grid[1]-1)*grid[0])\n",
        "\n",
        "    def forward(self, x):\n",
        "        out = self.linear(x)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1571557e",
      "metadata": {
        "id": "1571557e"
      },
      "source": [
        "Then, we can initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f162584",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:02.503758931Z",
          "start_time": "2023-07-03T16:16:02.452869940Z"
        },
        "id": "3f162584"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "62407a29",
      "metadata": {
        "id": "62407a29"
      },
      "source": [
        "## 4 Training and Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eebb2ef2",
      "metadata": {
        "id": "eebb2ef2"
      },
      "source": [
        "The core capability of PyEPO is to build optimization models with GurobiPy, Pyomo, or any other solvers and algorithms, then embed the optimization model into an artificial neural network for the end-to-end training. For this purpose, PyEPO implements **SPO+ loss** and **differentiable Black-Box optimizer**, **differentiable perturbed optimizer**, **Fenchel-Young loss with Perturbation**, **Noise Contrastive Estimation**, and **Learning to Rank** as PyTorch autograd modules.\n",
        "\n",
        "We will train and test the above aproaches."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67152388",
      "metadata": {
        "id": "67152388"
      },
      "source": [
        "### 4.1 Introduction to Metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3731def8",
      "metadata": {
        "id": "3731def8"
      },
      "source": [
        "``pyepo.metric.regret`` is to evaluate model performance. Regret (also called SPO loss) $l_{Regret}(\\hat{\\mathbf{c}}, \\mathbf{c}) = \\mathbf{c}^T \\mathbf{w}^*_{\\hat{\\mathbf{c}}} - z^*_{\\mathbf{c}}$ aims to measure the error in decision-making. It evaluates the distance between the objective value of the solution from predicted cost $\\hat{\\mathbf{c}}$ and the true optimal objective value $z^*_{c}$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01a18cb5",
      "metadata": {
        "id": "01a18cb5"
      },
      "source": [
        "To calculate regret, ``pyepo.metric.regret`` requires:\n",
        "- ``predmodel``: a regression neural network for cost prediction\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``dataloader``: PyTorch dataloader from optDataset to evaluate\n",
        "\n",
        "The following code block is an example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d92d811a",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:10.429107030Z",
          "start_time": "2023-07-03T16:16:08.916477331Z"
        },
        "id": "d92d811a"
      },
      "outputs": [],
      "source": [
        "import pyepo\n",
        "regret = pyepo.metric.regret(reg, optmodel, loader_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dc33c815",
      "metadata": {
        "id": "dc33c815"
      },
      "source": [
        "### 4.2 Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3860d36b",
      "metadata": {
        "id": "3860d36b"
      },
      "source": [
        "Define function to train model with different methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f4d57e9",
      "metadata": {
        "id": "5f4d57e9"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "\n",
        "# train model\n",
        "def trainModel(reg, loss_func, method_name, loader_train, num_epochs=20, lr=1e-2):\n",
        "    # set adam optimizer\n",
        "    optimizer = torch.optim.Adam(reg.parameters(), lr=lr)\n",
        "    # train mode\n",
        "    reg.train()\n",
        "    # init log\n",
        "    loss_log = []\n",
        "    loss_log_regret = [pyepo.metric.regret(reg, optmodel, loader_test)]\n",
        "    # init elpased time\n",
        "    elapsed = 0\n",
        "    for epoch in range(num_epochs):\n",
        "        # start timing\n",
        "        tick = time.time()\n",
        "        # load data\n",
        "        for i, data in enumerate(loader_train):\n",
        "            x, c, w, z = data\n",
        "            # cuda\n",
        "            if torch.cuda.is_available():\n",
        "                x, c, w, z = x.cuda(), c.cuda(), w.cuda(), z.cuda()\n",
        "            # forward pass\n",
        "            cp = reg(x)\n",
        "            if method_name == \"spo+\":\n",
        "                loss = loss_func(cp, c, w, z)\n",
        "            if method_name in [\"ptb\", \"pfy\", \"imle\", \"nce\", \"cmap\"]:\n",
        "                loss = loss_func(cp, w)\n",
        "            if method_name in [\"dbb\", \"nid\"]:\n",
        "                loss = loss_func(cp, c, z)\n",
        "            if method_name == \"ltr\":\n",
        "                loss = loss_func(cp, c)\n",
        "            # backward pass\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            # record time\n",
        "            tock = time.time()\n",
        "            elapsed += tock - tick\n",
        "            # log\n",
        "            loss_log.append(loss.item())\n",
        "        regret = pyepo.metric.regret(reg, optmodel, loader_test)\n",
        "        loss_log_regret.append(regret)\n",
        "        print(\"Epoch {:2},  Loss: {:9.4f},  Regret: {:7.4f}%\".format(epoch+1, loss.item(), regret*100))\n",
        "    print(\"Total Elapsed Time: {:.2f} Sec.\".format(elapsed))\n",
        "    return loss_log, loss_log_regret"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c5d241e8",
      "metadata": {
        "id": "c5d241e8"
      },
      "source": [
        "Define functions that visualize the learning curves."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0449b18a",
      "metadata": {
        "id": "0449b18a"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# EDIT-kNN: We adjust this function so we can compare the regular loss with the kNN loss\n",
        "def visLearningCurve(loss_log, loss_log_regret, loss_log_knn, loss_log_regret_knn):\n",
        "    # create figure and subplots\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,4))\n",
        "\n",
        "    # draw plot for training loss\n",
        "    ax1.plot(loss_log, color=\"c\", lw=2, label=\"Regular Loss\")\n",
        "    ax1.plot(loss_log_knn, color=\"r\", lw=2, label=\"kNN Loss\")\n",
        "    ax1.legend()\n",
        "    ax1.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
        "    ax1.set_xlabel(\"Iters\", fontsize=16)\n",
        "    ax1.set_ylabel(\"Loss\", fontsize=16)\n",
        "    ax1.set_title(\"Learning Curve on Training Set\", fontsize=16)\n",
        "\n",
        "    # draw plot for regret on test\n",
        "    ax2.plot(loss_log_regret, color=\"c\", ls=\"--\", lw=2, label=\"Regular Loss\")\n",
        "    ax2.plot(loss_log_regret_knn, color=\"r\", ls=\"--\", lw=2, label=\"kNN Loss\")\n",
        "    ax2.legend()\n",
        "    ax2.set_xticks(range(0, len(loss_log_regret), 2))\n",
        "    ax2.tick_params(axis=\"both\", which=\"major\", labelsize=12)\n",
        "    ax2.set_ylim(0, 0.8)\n",
        "    ax2.set_xlabel(\"Epochs\", fontsize=16)\n",
        "    ax2.set_ylabel(\"Regret\", fontsize=16)\n",
        "    ax2.set_title(\"Learning Curve on Test Set\", fontsize=16)\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aaacfd38",
      "metadata": {
        "id": "aaacfd38"
      },
      "source": [
        "### 4.3 Smart Predict-then-Optimize Loss+"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cba77671",
      "metadata": {
        "id": "cba77671"
      },
      "source": [
        "[Elmachtoub, A. N., & Grigas, P. (2022). Smart “predict, then optimize”. Management Science, 68(1), 9-26.](https://doi.org/10.1287/mnsc.2020.3922)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0fcbd223-f815-4d08-aa6c-24f14c8dad76",
      "metadata": {
        "id": "0fcbd223-f815-4d08-aa6c-24f14c8dad76"
      },
      "source": [
        "#### 4.3.1 Original SPO+"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "905f8b07",
      "metadata": {
        "id": "905f8b07"
      },
      "source": [
        "SPO+ Loss is a surrogate loss function of SPO Loss (Regret), which measures the decision error of optimization problem. For SPO/SPO+ Loss, the objective function is linear and constraints are known and fixed, but the cost vector need to be predicted from contextual data. The SPO+ Loss is convex with non-zero subgradient. Thus, allows us to design an algorithm based on stochastic gradient descent."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c03249b",
      "metadata": {
        "id": "0c03249b"
      },
      "source": [
        "First of all, we initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1586bcfe",
      "metadata": {
        "id": "1586bcfe"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "23d3f88e",
      "metadata": {
        "id": "23d3f88e"
      },
      "source": [
        "``pyepo.func.SPOPlus`` allows us to set a SPO+ loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94f93c65",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94f93c65",
        "outputId": "7376b7c8-2ae2-4f43-e080-5acaaa1f69df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init SPO+ loss\n",
        "spop = pyepo.func.SPOPlus(optmodel, processes=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6e32619",
      "metadata": {
        "id": "a6e32619"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89fe3311",
      "metadata": {
        "id": "89fe3311"
      },
      "source": [
        "Then, we can train a linear predictor with SPO+ loss to predict unknown cost coefficients and make decisions.\n",
        "\n",
        "To compute SPO+ loss, ``spop`` requires $\\mathbf{\\hat{c}}$, $\\mathbf{c}$, $\\mathbf{w}^*_{\\mathbf{c}}$, and $\\mathbf{z}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "25c259af",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25c259af",
        "outputId": "90f9cb2c-3d9f-4fea-8ba4-5956547bdb66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    8.9962,  Regret: 68.2792%\n",
            "Epoch  2,  Loss:    7.1083,  Regret: 52.8914%\n",
            "Epoch  3,  Loss:    4.4804,  Regret: 41.3257%\n",
            "Epoch  4,  Loss:    5.1921,  Regret: 30.7331%\n",
            "Epoch  5,  Loss:    1.2706,  Regret: 26.0826%\n",
            "Epoch  6,  Loss:    2.4131,  Regret: 21.8409%\n",
            "Epoch  7,  Loss:    5.4072,  Regret: 18.6524%\n",
            "Epoch  8,  Loss:    2.7117,  Regret: 17.2065%\n",
            "Epoch  9,  Loss:    0.7476,  Regret: 16.3042%\n",
            "Epoch 10,  Loss:    0.5109,  Regret: 15.6072%\n",
            "Epoch 11,  Loss:    1.1778,  Regret: 14.6836%\n",
            "Epoch 12,  Loss:    0.0297,  Regret: 14.1751%\n",
            "Epoch 13,  Loss:   10.1702,  Regret: 13.7504%\n",
            "Epoch 14,  Loss:    0.2949,  Regret: 14.0265%\n",
            "Epoch 15,  Loss:    0.5172,  Regret: 14.3956%\n",
            "Epoch 16,  Loss:    0.2518,  Regret: 14.1532%\n",
            "Epoch 17,  Loss:    0.5453,  Regret: 14.1886%\n",
            "Epoch 18,  Loss:    1.1527,  Regret: 13.8287%\n",
            "Epoch 19,  Loss:    1.9295,  Regret: 13.4326%\n",
            "Epoch 20,  Loss:    3.4877,  Regret: 13.1311%\n",
            "Total Elapsed Time: 24.28 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=spop, method_name=\"spo+\", loader_train=loader_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9bab9f18-e399-47ac-b085-2ba3f1aca74a",
      "metadata": {
        "id": "9bab9f18-e399-47ac-b085-2ba3f1aca74a"
      },
      "source": [
        "#### 4.3.2 SPO+ with kNN\n",
        "We compare the above result with the robust kNN loss.\n",
        "\n",
        "[Schutte, N., Postek, K., & Yorke-Smith, N.(2023). Robust losses for decision-focused learning. arXiv 2310.04328.](https://arxiv.org/abs/)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04ec9f01-90e8-4d4c-8c5a-f068e069462e",
      "metadata": {
        "id": "04ec9f01-90e8-4d4c-8c5a-f068e069462e"
      },
      "source": [
        "This loss is applied by considering cost...."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e4d3b9a0-0ebf-435d-9538-694fb7a969d9",
      "metadata": {
        "id": "e4d3b9a0-0ebf-435d-9538-694fb7a969d9"
      },
      "outputs": [],
      "source": [
        "# re-init predictive model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8c4b18c6-8f7f-4864-b55f-e81039cbfa9b",
      "metadata": {
        "id": "8c4b18c6-8f7f-4864-b55f-e81039cbfa9b"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b1e9db6a-cfbc-4f31-b540-55759acf6122",
      "metadata": {
        "id": "b1e9db6a-cfbc-4f31-b540-55759acf6122",
        "outputId": "649d6c2a-0dbd-4755-8cc4-1ccec1c29344",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    5.9004,  Regret: 83.9150%\n",
            "Epoch  2,  Loss:    4.3734,  Regret: 73.1691%\n",
            "Epoch  3,  Loss:   10.5238,  Regret: 59.5937%\n",
            "Epoch  4,  Loss:    3.6043,  Regret: 45.8688%\n",
            "Epoch  5,  Loss:    3.4360,  Regret: 35.4256%\n",
            "Epoch  6,  Loss:    5.4974,  Regret: 27.1937%\n",
            "Epoch  7,  Loss:    1.4175,  Regret: 23.1433%\n",
            "Epoch  8,  Loss:    0.9636,  Regret: 19.1462%\n",
            "Epoch  9,  Loss:    1.5410,  Regret: 15.1193%\n",
            "Epoch 10,  Loss:    1.4062,  Regret: 12.3654%\n",
            "Epoch 11,  Loss:    2.4998,  Regret: 11.2471%\n",
            "Epoch 12,  Loss:    1.0822,  Regret: 10.6280%\n",
            "Epoch 13,  Loss:    1.0280,  Regret: 10.8835%\n",
            "Epoch 14,  Loss:    2.1398,  Regret: 11.3204%\n",
            "Epoch 15,  Loss:    1.4318,  Regret: 11.6472%\n",
            "Epoch 16,  Loss:    0.9550,  Regret: 11.9367%\n",
            "Epoch 17,  Loss:    0.5294,  Regret: 12.1971%\n",
            "Epoch 18,  Loss:    1.0646,  Regret: 11.9935%\n",
            "Epoch 19,  Loss:    0.5200,  Regret: 11.1292%\n",
            "Epoch 20,  Loss:    0.5910,  Regret: 10.8611%\n",
            "Total Elapsed Time: 24.01 Sec.\n"
          ]
        }
      ],
      "source": [
        "#train\n",
        "loss_log_knn, loss_log_regret_knn = trainModel(reg, loss_func=spop, method_name=\"spo+\", loader_train=loader_train_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84305b24-b136-4312-b02b-058dec41f626",
      "metadata": {
        "id": "84305b24-b136-4312-b02b-058dec41f626"
      },
      "source": [
        "#### Learning curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9b0dee9-ff9f-49fc-b4cb-98b8f80573c1",
      "metadata": {
        "id": "d9b0dee9-ff9f-49fc-b4cb-98b8f80573c1",
        "outputId": "d8d30ae5-2ad9-4f97-a08c-97386a6b4c12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret, loss_log_knn, loss_log_regret_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3dee3614",
      "metadata": {
        "id": "3dee3614"
      },
      "source": [
        "### 4.4 Perturbed Fenchel-Young Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89a1a70b",
      "metadata": {
        "id": "89a1a70b"
      },
      "source": [
        "[Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J. P., & Bach, F. (2020). Learning with differentiable pertubed optimizers. Advances in neural information processing systems, 33, 9508-9519.](https://papers.nips.cc/paper/2020/hash/6bb56208f672af0dd65451f869fedfd9-Abstract.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5614aeee-7261-4213-ae8d-4879e26b95bb",
      "metadata": {
        "id": "5614aeee-7261-4213-ae8d-4879e26b95bb"
      },
      "source": [
        "#### 4.4.1 Orignal PFYL"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7332f0b2-eb79-4b46-8aa4-107652e660ef",
      "metadata": {
        "id": "7332f0b2-eb79-4b46-8aa4-107652e660ef"
      },
      "source": [
        "Perturbed Fenchel-Young loss (PYFL) uses perturbation techniques with Monte-Carlo samples. The use of the loss improves the algorithmic by the specific expression of the gradients of the loss. For the perturbed optimizer, the cost vector need to be predicted from contextual data and are perturbed with Gaussian noise. The Fenchel-Young loss allows to directly optimize a loss between the features and solutions with less computation. Thus, allows us to design an algorithm based on stochastic gradient descent."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b20cf56",
      "metadata": {
        "id": "5b20cf56"
      },
      "source": [
        "First of all, we initialize the predictor."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8353c159",
      "metadata": {
        "id": "8353c159"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89ad1b86",
      "metadata": {
        "id": "89ad1b86"
      },
      "source": [
        "``pyepo.func.perturbedFenchelYoung`` allows us to set a Fenchel-Young loss for training, which requires parameters:\n",
        "- ``optmodel``: a PyEPO optimization model\n",
        "- ``n_samples``: number of Monte-Carlo samples\n",
        "- ``sigma``:  the amplitude of the perturbation for costs\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of the cores\n",
        "- ``seed``: random state seed for perturbations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e1ba873",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e1ba873",
        "outputId": "d7d14a5a-fad0-4d0a-fe28-7bf6a7914ee5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "# init pfyl loss\n",
        "pfy = pyepo.func.perturbedFenchelYoung(optmodel, n_samples=3, sigma=1.0, processes=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95ababe4",
      "metadata": {
        "id": "95ababe4"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b6deb950",
      "metadata": {
        "id": "b6deb950"
      },
      "source": [
        "Then, we can train a linear predictor with Fenchel-Young loss to predict unknown cost coefficients and make decisions.\n",
        "\n",
        "To compute Fenchel-Young loss, ``pfy`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{w}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cc50c3d8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc50c3d8",
        "outputId": "41665e65-7e63-40c1-e1b9-7b01e43c398d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    9.5556,  Regret: 56.2476%\n",
            "Epoch  2,  Loss:    8.1111,  Regret: 46.3983%\n",
            "Epoch  3,  Loss:    5.7778,  Regret: 37.9226%\n",
            "Epoch  4,  Loss:    4.6667,  Regret: 32.3494%\n",
            "Epoch  5,  Loss:    7.3333,  Regret: 30.0932%\n",
            "Epoch  6,  Loss:    6.7778,  Regret: 27.1591%\n",
            "Epoch  7,  Loss:    5.7222,  Regret: 24.3806%\n",
            "Epoch  8,  Loss:    4.0000,  Regret: 22.8114%\n",
            "Epoch  9,  Loss:    6.0556,  Regret: 21.9359%\n",
            "Epoch 10,  Loss:    5.0556,  Regret: 20.3327%\n",
            "Epoch 11,  Loss:    7.8889,  Regret: 18.7409%\n",
            "Epoch 12,  Loss:    4.7222,  Regret: 17.8722%\n",
            "Epoch 13,  Loss:    3.4444,  Regret: 17.7221%\n",
            "Epoch 14,  Loss:    3.0556,  Regret: 17.5042%\n",
            "Epoch 15,  Loss:    7.5000,  Regret: 16.7729%\n",
            "Epoch 16,  Loss:    4.4444,  Regret: 16.7208%\n",
            "Epoch 17,  Loss:    3.4444,  Regret: 16.5208%\n",
            "Epoch 18,  Loss:    2.6111,  Regret: 16.5980%\n",
            "Epoch 19,  Loss:    3.8889,  Regret: 16.6461%\n",
            "Epoch 20,  Loss:    4.2222,  Regret: 16.4853%\n",
            "Total Elapsed Time: 45.39 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=pfy, method_name=\"pfy\", loader_train=loader_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "712410c8-4044-4242-b198-b839a0f8d815",
      "metadata": {
        "id": "712410c8-4044-4242-b198-b839a0f8d815"
      },
      "source": [
        "#### 4.4.2 PFYL with kNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "554a9380-2c6b-43f5-a855-8e5512dc58db",
      "metadata": {
        "id": "554a9380-2c6b-43f5-a855-8e5512dc58db"
      },
      "outputs": [],
      "source": [
        "# re-init predictive model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eddfc94e-581a-49dd-885c-1441f850b439",
      "metadata": {
        "id": "eddfc94e-581a-49dd-885c-1441f850b439"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a93f941-0cb7-41fa-9510-ec0e5f25ee58",
      "metadata": {
        "id": "6a93f941-0cb7-41fa-9510-ec0e5f25ee58",
        "outputId": "a0d71eac-2b1c-4ba1-df72-76cab595aa6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    6.0033,  Regret: 81.5835%\n",
            "Epoch  2,  Loss:    6.0833,  Regret: 66.7092%\n",
            "Epoch  3,  Loss:    7.0733,  Regret: 57.5385%\n",
            "Epoch  4,  Loss:    3.8644,  Regret: 46.3386%\n",
            "Epoch  5,  Loss:    3.2978,  Regret: 37.7809%\n",
            "Epoch  6,  Loss:    5.1678,  Regret: 30.6231%\n",
            "Epoch  7,  Loss:    3.3917,  Regret: 25.4510%\n",
            "Epoch  8,  Loss:    4.1417,  Regret: 20.3979%\n",
            "Epoch  9,  Loss:    3.3456,  Regret: 17.3444%\n",
            "Epoch 10,  Loss:    1.2411,  Regret: 16.5571%\n",
            "Epoch 11,  Loss:    5.2333,  Regret: 14.8810%\n",
            "Epoch 12,  Loss:    1.8744,  Regret: 14.0616%\n",
            "Epoch 13,  Loss:    1.9911,  Regret: 13.5804%\n",
            "Epoch 14,  Loss:    4.3289,  Regret: 12.8349%\n",
            "Epoch 15,  Loss:    3.2589,  Regret: 12.4936%\n",
            "Epoch 16,  Loss:    1.3078,  Regret: 12.2152%\n",
            "Epoch 17,  Loss:    2.0189,  Regret: 12.0566%\n",
            "Epoch 18,  Loss:    2.7361,  Regret: 12.1883%\n",
            "Epoch 19,  Loss:    2.3578,  Regret: 11.9840%\n",
            "Epoch 20,  Loss:    0.6894,  Regret: 11.8946%\n",
            "Total Elapsed Time: 44.12 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log_knn, loss_log_regret_knn = trainModel(reg, loss_func=pfy, method_name=\"pfy\", loader_train=loader_train_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1e2e03ba-f9af-419f-9c25-b82ebb7f5616",
      "metadata": {
        "id": "1e2e03ba-f9af-419f-9c25-b82ebb7f5616"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "715317e7-8b31-4273-ac6f-cbd5eb03651c",
      "metadata": {
        "id": "715317e7-8b31-4273-ac6f-cbd5eb03651c",
        "outputId": "a3990b9c-f191-443c-9617-c4d4f7539d55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret, loss_log_knn, loss_log_regret_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e74c152",
      "metadata": {
        "id": "0e74c152"
      },
      "source": [
        "### 4.5 Contrastive Estimation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8b9a1d00",
      "metadata": {
        "id": "8b9a1d00"
      },
      "source": [
        "[Mulamba, M., Mandi, J., Diligenti, M., Lombardi, M., Bucarey, V., & Guns, T. (2021). Contrastive losses and solution caching for predict-and-optimize. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.](https://www.ijcai.org/proceedings/2021/390)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fdca6a1",
      "metadata": {
        "id": "7fdca6a1"
      },
      "source": [
        "#### 4.5.1 Original NCE"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7eaab10b",
      "metadata": {
        "id": "7eaab10b"
      },
      "source": [
        "It uses a noise contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. For the NCE, the cost vector needs to be predicted from contextual data and maximizes the separation of the probability of the optimal solution."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b407567",
      "metadata": {
        "id": "5b407567"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b447a5f7",
      "metadata": {
        "id": "b447a5f7"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "841c0a44",
      "metadata": {
        "id": "841c0a44"
      },
      "source": [
        "``pyepo.func.NCE`` allows us to use a noise contrastive estimiation loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "19fc8c2b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19fc8c2b",
        "outputId": "e896e4fa-0ff4-4be0-953c-246bd66ca2c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "nce = pyepo.func.NCE(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b831428",
      "metadata": {
        "id": "0b831428"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e15a116",
      "metadata": {
        "id": "4e15a116"
      },
      "source": [
        "To compute noise contrastive estimation loss, ``nce`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{w}^*_{\\mathbf{c}}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b863fea1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b863fea1",
        "outputId": "e571e408-efb7-462f-8cc1-c9a2cc427112"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:   -1.7121,  Regret: 60.8562%\n",
            "Epoch  2,  Loss:   -2.8317,  Regret: 51.7757%\n",
            "Epoch  3,  Loss:   -0.9225,  Regret: 44.1602%\n",
            "Epoch  4,  Loss:   -4.6647,  Regret: 38.4251%\n",
            "Epoch  5,  Loss:   -5.6253,  Regret: 31.6764%\n",
            "Epoch  6,  Loss:   -1.3613,  Regret: 28.8473%\n",
            "Epoch  7,  Loss:    0.2501,  Regret: 26.3436%\n",
            "Epoch  8,  Loss:   -9.3533,  Regret: 24.5924%\n",
            "Epoch  9,  Loss:   -8.1539,  Regret: 24.6801%\n",
            "Epoch 10,  Loss:   -6.4699,  Regret: 24.1026%\n",
            "Epoch 11,  Loss:   -7.5189,  Regret: 23.0819%\n",
            "Epoch 12,  Loss:   -9.0077,  Regret: 22.4976%\n",
            "Epoch 13,  Loss:   -8.3384,  Regret: 21.8102%\n",
            "Epoch 14,  Loss:  -11.5821,  Regret: 21.1863%\n",
            "Epoch 15,  Loss:   -5.8641,  Regret: 20.7207%\n",
            "Epoch 16,  Loss:   -7.2606,  Regret: 20.4248%\n",
            "Epoch 17,  Loss:  -17.8987,  Regret: 20.4487%\n",
            "Epoch 18,  Loss:   -9.2225,  Regret: 20.1951%\n",
            "Epoch 19,  Loss:  -15.3970,  Regret: 20.1231%\n",
            "Epoch 20,  Loss:   -6.0159,  Regret: 20.0481%\n",
            "Total Elapsed Time: 2.82 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=nce, method_name=\"nce\", loader_train=loader_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e502d4d-cd99-48db-af5f-274d00ff4ef1",
      "metadata": {
        "id": "0e502d4d-cd99-48db-af5f-274d00ff4ef1"
      },
      "source": [
        "#### 4.5.2 NCE with kNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c406c4b-a566-403a-ba18-c31c6dc11958",
      "metadata": {
        "id": "0c406c4b-a566-403a-ba18-c31c6dc11958"
      },
      "outputs": [],
      "source": [
        "# re-init predictive model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56f7ce92-9575-486d-9e0b-f60ad1d422db",
      "metadata": {
        "id": "56f7ce92-9575-486d-9e0b-f60ad1d422db"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "75efecea-bc12-462c-bded-6b5c006632cd",
      "metadata": {
        "id": "75efecea-bc12-462c-bded-6b5c006632cd",
        "outputId": "b4084ad2-0199-4953-dc35-fda77a7ae97f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.1927,  Regret: 92.1281%\n",
            "Epoch  2,  Loss:   -0.7710,  Regret: 78.9825%\n",
            "Epoch  3,  Loss:   -1.4706,  Regret: 69.9776%\n",
            "Epoch  4,  Loss:   -1.8483,  Regret: 60.4449%\n",
            "Epoch  5,  Loss:   -2.8949,  Regret: 47.9218%\n",
            "Epoch  6,  Loss:   -2.1602,  Regret: 37.4722%\n",
            "Epoch  7,  Loss:   -3.4456,  Regret: 32.2370%\n",
            "Epoch  8,  Loss:   -2.9797,  Regret: 29.3886%\n",
            "Epoch  9,  Loss:   -3.6804,  Regret: 27.0315%\n",
            "Epoch 10,  Loss:   -2.8635,  Regret: 25.9881%\n",
            "Epoch 11,  Loss:   -4.1543,  Regret: 25.7754%\n",
            "Epoch 12,  Loss:   -7.9768,  Regret: 24.3415%\n",
            "Epoch 13,  Loss:   -5.4470,  Regret: 24.1090%\n",
            "Epoch 14,  Loss:   -8.4020,  Regret: 23.8500%\n",
            "Epoch 15,  Loss:   -6.6382,  Regret: 23.1342%\n",
            "Epoch 16,  Loss:  -13.6440,  Regret: 22.0864%\n",
            "Epoch 17,  Loss:   -9.2922,  Regret: 21.4432%\n",
            "Epoch 18,  Loss:   -7.5885,  Regret: 20.7631%\n",
            "Epoch 19,  Loss:  -11.3075,  Regret: 20.9733%\n",
            "Epoch 20,  Loss:   -8.0130,  Regret: 20.8130%\n",
            "Total Elapsed Time: 1.85 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log_knn, loss_log_regret_knn = trainModel(reg, loss_func=nce, method_name=\"nce\", loader_train=loader_train_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f879771",
      "metadata": {
        "id": "8f879771"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "28228e56-fbd3-408d-b1d1-8a835f3118e5",
      "metadata": {
        "id": "28228e56-fbd3-408d-b1d1-8a835f3118e5",
        "outputId": "356b5a37-48c6-4c4d-f705-be5f6116afbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret, loss_log_knn, loss_log_regret_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6abb4845",
      "metadata": {
        "id": "6abb4845"
      },
      "source": [
        "### 4.6 Learning To Rank"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5cd0621d",
      "metadata": {
        "id": "5cd0621d"
      },
      "source": [
        "[Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Decision-focused learning: through the lens of learning to rank. Proceedings of the 39th International Conference on Machine Learning.](https://proceedings.mlr.press/v162/mandi22a.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf527d34",
      "metadata": {
        "id": "bf527d34"
      },
      "source": [
        "#### 4.6.1 Pairwise LTR"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7477432c",
      "metadata": {
        "id": "7477432c"
      },
      "source": [
        "An autograd module for pairwise learning to rank, where the goal is to learn an objective function that ranks a pool of feasible solutions correctly. For the pairwise LTR, the cost vector needs to be predicted from contextual data, and the loss learns the relative ordering of pairs of items."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ea1cde2b",
      "metadata": {
        "id": "ea1cde2b"
      },
      "source": [
        "First of all, we initialize the predictor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af18ca38",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:24.267717164Z",
          "start_time": "2023-07-03T16:16:18.452617457Z"
        },
        "id": "af18ca38"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "# init model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27d7caf5",
      "metadata": {
        "id": "27d7caf5"
      },
      "source": [
        "``pyepo.func.pairwiseLTR`` allows us to use a listwise learning to rank loss for training, which requires parameters:\n",
        "- ``optmodel``: an PyEPO optimization model\n",
        "- ``processes``: number of processors for multi-thread, 1 for single-core, 0 for all of cores\n",
        "- ``solve_ratio``: the ratio of new solutions computed during training\n",
        "- ``dataset``: a dataset to initialize the solution pool with. Usually this is simply the training set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "72ff1275",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:16:27.478976953Z",
          "start_time": "2023-07-03T16:16:27.473935240Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72ff1275",
        "outputId": "1e75b522-7fdf-42b6-9ddc-fc24dcbcec3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num of cores: 2\n"
          ]
        }
      ],
      "source": [
        "prltr = pyepo.func.pairwiseLTR(optmodel, processes=2, solve_ratio=0.05, dataset=dataset_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2eb36db3",
      "metadata": {
        "id": "2eb36db3"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4850ca91",
      "metadata": {
        "id": "4850ca91"
      },
      "source": [
        "To compute learning-to-rank loss, ``prltr`` requires $\\mathbf{\\hat{c}}$ and $\\mathbf{c}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "120e76ad",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-07-03T16:17:01.763318176Z",
          "start_time": "2023-07-03T16:16:32.516958317Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "120e76ad",
        "outputId": "60a01f94-e0b7-47b6-b2d5-d142f6dd75e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.6855,  Regret: 86.2206%\n",
            "Epoch  2,  Loss:    0.8593,  Regret: 78.6917%\n",
            "Epoch  3,  Loss:    0.3913,  Regret: 72.4211%\n",
            "Epoch  4,  Loss:    0.2725,  Regret: 64.4754%\n",
            "Epoch  5,  Loss:    0.0413,  Regret: 54.6250%\n",
            "Epoch  6,  Loss:    0.1040,  Regret: 44.1258%\n",
            "Epoch  7,  Loss:    0.2600,  Regret: 36.5674%\n",
            "Epoch  8,  Loss:    0.0363,  Regret: 31.9835%\n",
            "Epoch  9,  Loss:    0.0099,  Regret: 27.5555%\n",
            "Epoch 10,  Loss:    0.0359,  Regret: 26.1819%\n",
            "Epoch 11,  Loss:    0.0434,  Regret: 23.9598%\n",
            "Epoch 12,  Loss:    0.0632,  Regret: 22.7887%\n",
            "Epoch 13,  Loss:    0.0034,  Regret: 22.6724%\n",
            "Epoch 14,  Loss:    0.0058,  Regret: 21.6986%\n",
            "Epoch 15,  Loss:    0.0004,  Regret: 21.7587%\n",
            "Epoch 16,  Loss:    0.0078,  Regret: 21.5190%\n",
            "Epoch 17,  Loss:    0.0248,  Regret: 21.1556%\n",
            "Epoch 18,  Loss:    0.0000,  Regret: 21.2188%\n",
            "Epoch 19,  Loss:    0.0054,  Regret: 21.0201%\n",
            "Epoch 20,  Loss:    0.0095,  Regret: 20.8903%\n",
            "Total Elapsed Time: 5.11 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log, loss_log_regret = trainModel(reg, loss_func=prltr, method_name=\"ltr\", loader_train=loader_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7078715f-b394-480a-a88b-e7403b548f87",
      "metadata": {
        "id": "7078715f-b394-480a-a88b-e7403b548f87"
      },
      "source": [
        "#### 4.6.2 Pairwise LTR with kNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ee8abffb-ffef-4aa5-b314-6563128cfca0",
      "metadata": {
        "id": "ee8abffb-ffef-4aa5-b314-6563128cfca0"
      },
      "outputs": [],
      "source": [
        "# re-init predictive model\n",
        "reg = LinearRegression()\n",
        "# cuda\n",
        "if torch.cuda.is_available():\n",
        "    reg = reg.cuda()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99ade727-53b3-4c03-a420-3a5f5b9648c3",
      "metadata": {
        "id": "99ade727-53b3-4c03-a420-3a5f5b9648c3"
      },
      "source": [
        "##### Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51937e68-33bd-46a8-aaf1-78a20ebf27de",
      "metadata": {
        "id": "51937e68-33bd-46a8-aaf1-78a20ebf27de",
        "outputId": "94bbebbf-9cce-431e-ee1a-879faa138a83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch  1,  Loss:    0.4109,  Regret: 82.0071%\n",
            "Epoch  2,  Loss:    0.4974,  Regret: 64.4352%\n",
            "Epoch  3,  Loss:    0.3202,  Regret: 51.3592%\n",
            "Epoch  4,  Loss:    0.1772,  Regret: 44.3244%\n",
            "Epoch  5,  Loss:    0.1242,  Regret: 36.1179%\n",
            "Epoch  6,  Loss:    0.1945,  Regret: 33.1601%\n",
            "Epoch  7,  Loss:    0.0422,  Regret: 31.4998%\n",
            "Epoch  8,  Loss:    0.0279,  Regret: 30.0317%\n",
            "Epoch  9,  Loss:    0.0502,  Regret: 27.8142%\n",
            "Epoch 10,  Loss:    0.0044,  Regret: 25.9844%\n",
            "Epoch 11,  Loss:    0.0162,  Regret: 24.6788%\n",
            "Epoch 12,  Loss:    0.0104,  Regret: 24.5019%\n",
            "Epoch 13,  Loss:    0.0007,  Regret: 24.4174%\n",
            "Epoch 14,  Loss:    0.0139,  Regret: 23.4380%\n",
            "Epoch 15,  Loss:    0.0209,  Regret: 23.6843%\n",
            "Epoch 16,  Loss:    0.0083,  Regret: 22.8899%\n",
            "Epoch 17,  Loss:    0.0068,  Regret: 22.1583%\n",
            "Epoch 18,  Loss:    0.0000,  Regret: 20.7506%\n",
            "Epoch 19,  Loss:    0.0228,  Regret: 20.1178%\n",
            "Epoch 20,  Loss:    0.0000,  Regret: 19.3747%\n",
            "Total Elapsed Time: 4.67 Sec.\n"
          ]
        }
      ],
      "source": [
        "loss_log_knn, loss_log_regret_knn = trainModel(reg, loss_func=prltr, method_name=\"ltr\", loader_train=loader_train_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ddef1bb",
      "metadata": {
        "id": "9ddef1bb"
      },
      "source": [
        "##### Learning Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "86255b9a-db9a-465f-af38-7b06ef9d0d63",
      "metadata": {
        "id": "86255b9a-db9a-465f-af38-7b06ef9d0d63",
        "outputId": "c790c4fc-00cd-4e09-a034-50ee7cadae15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visLearningCurve(loss_log, loss_log_regret, loss_log_knn, loss_log_regret_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "79bc435f-15c2-4d7a-be0b-138ad0d92a2d",
      "metadata": {
        "id": "79bc435f-15c2-4d7a-be0b-138ad0d92a2d"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}